An adaptive antenna array is an array of antennas connected to a communication receiver, which operates by combining the signals received by the antennas in order to optimize, in an adaptive fashion, the receive characteristics of the array. By weighting and then summing the multiple antenna signals, the adaptive antenna array adapts its angular response, sometimes called the array pattern, in response to changes in the propagation environment. While operating, the adaptive antenna attempts to maximize the reception of the signal received from a desired transmitting device, while simultaneously minimizing the effects of unwanted interfering signals and noise. In a communication system, the interference suppression capability of an adaptive antenna array offers the potential to reduce co-channel interference, improve coverage quality, and increase overall system capacity.
In a communication system, it is known in the art for a transmitter to transmit training sequences, also known as pilot codes or pilot symbols, to a receiver. The receiving device has prior knowledge of the nature of the transmitted training sequence and uses the received training sequence to perform tasks such as carrier recovery, channel estimation, and other related tasks that are known in the art for maintaining a high-quality communication link. In order to exploit the allocated spectrum most efficiently, it would be advantageous to minimize the percentage of the transmitted symbols that are pilot symbols.
When used with very short training sequences, however, conventional diversity combining techniques, also known as adaptive array combining techniques, suffer a performance degradation due to degraded covariance and channel gain estimates. Short training sequences often do not provide enough processing gain to adequately suppress any interference present in the channel measurements used to form these estimates. Generally, the sample matrix inversion (SMI) algorithm, which is a known method for calculating the branch combining weights of an adaptive array, requires a training sequence length of two times the number of antenna elements to achieve an average signal-to-interference plus noise ratio (SINR) within 3 dB of the theoretically optimal level. Lengthening of the training sequence allows for more accurate signal decoding, but results in lower data throughput because of higher overhead.
Another problem that exists with conventional diversity combining techniques is the loss of channel state information in a soft decision output signal. Many communication systems employ forward error correction coding to reduce the bit error rate of the transmitted information. Soft decision decoding of such codes is known in the art to be superior to hard decision decoding. In fading channels, however, conventional diversity combining techniques distort the channel state information in the soft decision output, which leads to an increase in the bit error rate at the output of the decoder. The distortion is caused by typical minimum-mean-square-error (MMSE) diversity combining algorithms attempting to minimize the mean-square error between the array output and a known training or reference sequence stored in the receiver. As a result, the diversity combining algorithm automatically scales the array output signal to have amplitude characteristics similar to the stored training sequence even when the array input signals are in a deep fade. Thus, the amplitude characteristics of the array output signals do not reflect the actual channel state information of the combined signal, leading to degraded performance in the soft-decision decoder.
Accordingly, there is a need for an apparatus and method that utilizes a short training sequence and restores the soft decision channel state information.